Python Packages For Datamining

Python Packages For Datamining

Just because you have a “hammer”, doesn’t mean that every problem you come across will be a “nail”.

The intelligent key thing is when you use the same hammer to solve what ever problem you came across. Like the same way when we intended to solve a datamining problem we will face so many issues but we can solve them by using python in a intelligent way.

In very next post I am going to wet your hands to solve one interesting datamining problem using python programming language. So in this post I am going to explain you about some powerful python weapons( packages )

Before stepping directly into python packages let me clear you a doubt which is rotating in your mind right now. Why python ?

Why Python ?

We all know that python is powerful programming language ,But what does that mean, exactly? What makes python a powerful programming language?

Python is Easy

Universally Python has gained reputation because of its easy learning. The syntax of python programming language is designed to be easily readable. Python has significant popularity in scientific computing. The people working in this field are scientists first, and programmers second.

Python is Efficient

Now a days we are working on bulk amount of data popularly know as BIG DATA. The more data you have to process, the more important it becomes to manage the memory you use. Here python will work very efficiently.

Python is Fast

We all know Python is an interpreted language, we may think that it may be slow but some amazing work has been done over the past years to improve Python’s performance. My point is that if you want to do high-performance computing, Python is a viable option today.

NumPy

About:

NumPy is the fundamental package for scientific computing with Python. It contains among other things.NumPy is an extension to the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications.

SciPy

About:

SciPy (pronounced “Sigh Pie”) is open-source software for mathematics, science, and engineering. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, Scipy is one of the most need one.

Pandas

About:

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.

Pandas is well suited for many different kinds of data:

Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet.

Ordered and unordered (not necessarily fixed-frequency) time series data.

Matplotlib

About:
matplotlib is a plotting library for the Python programming language and its NumPy numerical mathematics extension. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB. SciPy makes use of matplotlib.

Ipython

IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history. IPython currently provides the following features:

Powerful interactive shells (terminal and Qt-based).

A browser-based notebook with support for code, text, mathematical expressions, inline plots and other rich media.

scikit-learn

The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. Its name stems from the notion that it is a “SciKit” (SciPy Toolkit), a separately-developed and distributed third-party extension to SciPy. The original codebase was later extensively rewritten by other developers. Of the various scikits, scikit-learn as well as scikit-image were described as “well-maintained and popular” in November 2012.

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